本論文是基於McCartney的有霧天氣成像模型並提出新的透射率估算方式，且從文獻可以得知，如何找到透射率是除霧的主要問題。本研究利用向量量化的方法將大量的有霧、無霧影像當作我們的資料庫，再以LBG演算法訓練資料庫，最後以匹配的方式找到透射率。速度的部分，以引導濾波結合下採樣的方式提高速度，但卻能保持除霧後的影像品質相差無幾。在訓練編碼簿及預估透射率的時候，我們以RGB色彩、黑暗通道以及對比度來當作配對特徵，利用黑暗通道跟對比特徵的互補性，我們可以準確的找出正確的透射率。
實驗結果顯示，我們可以有效的避免沒有霧氣覆蓋且亮度較高的物體過度去霧，並且保持前景部分的自然度，整體影像更自然，且細節也能清楚地呈現出來。

The proposed method is based on McCartney’s optical haze model and uses a novel approach to estimate transmission. According to the literature, the major problem is estimating the transmission in the model-based method. This study trains plenty of haze-free and hazy images as codebooks with LBG algorithm. Then it is used to estimate transmission with matching. In order to speed up the process, the input image is down-sampled before refining with guided image filter. It not only can reduce processing time but also can preserve the quality of restored images. RGB, dark channel, and contrast values are regarded as features while training codebooks and estimating transmission. The transmission can be selected accurately because dark channel and contrast feature have complementarity.
The experiment results show that the haze-free high-intensity objects can avoid over dehazing and keep the foreground of restored images more natural. The details of recovered images are also clearer.

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